Global Particle Physics Excellence Awards

  Introduction Accurate traffic flow prediction forms the backbone of modern intelligent transportation systems , ensuring safer, faster, and more efficient urban mobility. However, the complexity of road networks, characterized by dynamic spatial structures and rapidly changing temporal patterns, poses significant challenges for predictive modeling. Conventional spatial-temporal graph neural networks (STGNNs) often fall short in effectively integrating these factors across various temporal and spatial scales. To overcome these limitations, the Frequency-Aware Interactive Spatial-Temporal Graph Convolutional Network (FISTGCN) introduces a novel deep learning architecture capable of capturing both long-term stability and short-term variations in traffic dynamics through adaptive and frequency-aware mechanisms . Limitations of Existing STGNN Models Traditional STGNN frameworks primarily focus on static graph structures or fixed temporal intervals, leading to an incomplete underst...

Introduction Children with intoeing gait face an elevated risk of tripping , leading to physical injuries, limited mobility, and psychological stress. Traditional gait analysis in laboratory environments fails to capture real-world tripping patterns , which limits the accuracy of clinical assessment and treatment outcomes. This research introduces a wearable tripping monitor designed to quantify tripping events during daily activities. The study focuses on developing a compact, low-cost, and energy-efficient device that accurately logs tripping hazard events (THEs) and step counts over two weeks of regular movement, offering a valuable tool for clinicians and AI-based gait analysis models . Wearable System Design and Sensor Integration The wearable tripping monitor integrates multiple sensors to ensure precise detection of tripping events without compromising comfort or battery life. It combines a Radio Frequency Identification (RFID) reader, passive Near-Field Communication (NFC...

Introduction The advancement of optical sensing technologies has been significantly propelled by the development of metasurfaces , particularly metal–dielectric hybrid structures . Among them, the metal–dielectric hybrid tetramer metasurface has garnered widespread attention for its exceptional refractive index sensing capabilities. However, despite its potential, challenges remain in achieving a balance between a high Q-factor , polarization insensitivity, and multi-band tunability across visible to near-infrared spectra. To address these issues, the proposed metal–dielectric yurt tetramer metasurface introduces an innovative approach that enhances light–matter interactions through structural optimization and controlled perturbation, paving the way for ultra-sensitive refractive index detection. Design and Simulation Methodology The design of the metal–dielectric yurt tetramer metasurface is based on an intricate arrangement that combines metallic and dielectric components to man...

Introduction The Global Particle Physics Excellence Awards serve as a beacon of recognition for scientists who push the limits of discovery in the vast realm of particle physics. Through the Best Innovation Award, the initiative honors exceptional minds whose groundbreaking research redefines our understanding of the universe. This recognition encourages collaboration among researchers and fosters innovation across disciplines, ultimately inspiring a new generation of physicists to explore the unknown and transform theoretical concepts into real-world applications. Research and Technological Advancements The Best Innovation Award focuses on recognizing research that merges advanced theoretical physics with experimental breakthroughs. From quantum field studies to particle accelerator experiments, the award celebrates the synthesis of creativity and technology. Researchers contributing to high-energy physics, data modeling, or the development of next-generation detectors are acknowledge...

Introduction The inverse Judd–Ofelt (JO) formalism represents a major advancement in the analysis of rare-earth-doped materials , providing a pathway to extract meaningful spectroscopic parameters without the need for absolute absorption calibration . By anchoring only a single radiative lifetime , this approach yields the complete set of JO intensity parameters (Ω₂, Ω₄, Ω₆) and radiative transition rates directly from relative absorption spectra . Applied to rare-earth ions such as Er³⁺, Dy³⁺, and Sm³⁺ in oxyfluoride glass matrices , this model demonstrates remarkable consistency with traditional JO analysis results . The method simplifies data processing, reduces experimental complexity, and enables fast comparative studies of luminescent materials , particularly in systems where absolute absorption measurements are challenging. Methodological Approach in Inverse Judd–Ofelt Analysis The inverse JO technique relies on the analysis of normalized absorption band strengths obtained...

Introduction Graphene , a two-dimensional material with exceptional electrical, mechanical, and thermal properties, continues to attract significant attention in the fields of condensed matter physics and nanotechnology . Its unique electronic structure, characterized by massless Dirac fermions , allows for fascinating quantum phenomena such as strong temperature-dependent responses and nonlocal dielectric behavior . This research explores the temperature dependence of graphene’s spatially nonlocal response functions and their implications for Casimir and Casimir–Polder forces . By combining theoretical derivations within the framework of quantum field theory and the Dirac model , this study provides a deeper understanding of how temperature, energy gap, and chemical potential affect the electromagnetic interactions between graphene layers and atoms or nanoparticles . Polarization Tensor and Dirac Model The polarization tensor of graphene serves as a cornerstone for understandi...